论文标题
启用线性非高斯系统的开环随机最佳控制
Convexified Open-Loop Stochastic Optimal Control for Linear Non-Gaussian Systems
论文作者
论文摘要
我们考虑对线性动力学系统的随机最佳控制，并具有添加剂非高斯干扰。我们基于傅立叶变换和凸优化提出了一种新颖的无抽样方法，以将随机的最佳控制问题作为范围差异程序提出。与现有的基于力矩的方法相反，我们的方法调用了更高的时刻，从而减少了保守主义。我们采用分段仿射近似值和众所周知的凸连接过程，通过标准圆锥求解器有效地解决了所得的优化问题。我们证明，所提出的方法在计算上比现有基于粒子的方法和基于力矩的方法更快，而不会损害概率安全约束。
We consider stochastic optimal control of linear dynamical systems with additive non-Gaussian disturbance. We propose a novel, sampling-free approach, based on Fourier transformations and convex optimization, to cast the stochastic optimal control problem as a difference-of-convex program. In contrast to existing moment based approaches, our approach invokes higher moments, resulting in less conservatism. We employ piecewise affine approximations and the well-known convex-concave procedure, to efficiently solve the resulting optimization problem via standard conic solvers. We demonstrate that the proposed approach is computationally faster than existing particle based and moment based approaches, without compromising probabilistic safety constraints.
